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How does Income Affect the Probability of Obesity

The US has witnessed an increase in the cases of obesity especially in the last five years. The Center for Disease Control and Prevention (CDC) contends that as at 2017, the obesity prevalence rate was 41.4% and has since increased to an average of 42.8% among the adults above 60 years (Centre for Disease Control and Prevention, 2021). Demographically, CDC indicates that children aged 2 to 5 years and the elderly above 60 are placed at the greatest risk of getting obesity and its subsequent opportunistic conditions. If the trend is anything to go by, the rate of obesity especially the vulnerable population is set to spiral with subsequent implications such as chronic conditions and even death being witnessed. There are many factors that contribute to the increased cases of obesity. This essay seeks to explore the extent to which income levels affect the probability of obesity cases. The research question that will form the basis of the research will be “how does income affect the probability of obesity?

Analysis of Key Studies

Significant research has gone into exploring the existing correlation between obesity and low-income levels. Kim et al., (2018) used a systematic review analysis to demonstrate that income has a direct relationship with the prevalence of obesity. Despite this relationship, the author contends that the there is a possible reverse causality when looking at the two variables. Therefore, it is possible that obesity is also a cause for low income. This can be reflected when people are obese are subjected to discriminative practices that force them to work in low-income jobs as a result of public stigmatization. The systematic review carried out by relied on quantitative data to demonstrate that although low-income results in obesity cases as people who have from low income fail to meet physical and nutritional body needs, on the contrary, having obesity compromises performance and results in discriminatory practices that eventually subject one to low-income jobs.

The income-obesity relationship has as well been explored by Zhou, M. (2021). In the article, the authors looks at the ongoing debate as to whether it is high income or low-income earners that are vulnerable to contracting diabetes. According to Zhou, the debate is as a result of the failure to account fully for the macro-level social contexts and that the income-obesity relationship cannot be directly discussed since it varies from one society to another. The relationship is defined by the macro-level social issues whose uniformity is not guaranteed. Consequently, the author discusses the need to look at either side when assessing how income contributes to obesity. Zhou contends that using multilevel models such as the 2011 Module on Health and Health Care of the INTERNATIONAL Social Survey Program, it is possible to assess the complex relationship between economic abilities and the emergence of obesity among patients. Zhou concluded that income is likely to depress obesity only in countries that are highly developed and industrialized while in most countries, there is no direct correlation between income and the emergence of cases of obesity.

Obesity has as well been a major public health as well as a public policy issue with significant private and social costs. Kim et al., (2018) states that the literature on causes of obesity has already underscored some of the issues that can directly be linked o the prevalence of obesity. Among the many issues, poverty is regarded as one of the key factors that contribute to obesity especially in rich countries. Nevertheless, as the author argues, the link between obesity and income levels is rather complex than it already appears. The relationship can be either positive or negative and can as well change as countries change socio-economically over time. Using an empirical literature review, Tang et al. (2021) demonstrates that it requires an even deeper literature review analysis to explore the existing correlation between economic abilities and the emergence of cases of obesity. The link between obesity and economic abilities remains elusive and there is need for researchers to provide a deeper analysis of the two aspects with an even more open mind.

In the Chinese context, Tang et al. (2021) employs the concentration index and decomposition analysis to explore the prevalence of obesity and its association with a person’s economic status. The focus of the paper was to assess the prevalence of obesity among Chinese workers aged 16 to 65 and how their social status influences the prevalence of diabetes. The findings show that using BMI analysis, diabetes varies by gender and residency. From the results, obesity seemed to be higher among people with higher economic status. The gender issue emerges strongly and contributes to the prevalence and distribution of diabetes conditions. The author suggests that policy makers have a more targeted response approach when establishing key response mechanisms. Bentley et al. (2018) agrees that there has been an upward trend of diabetes among low-income earners. Across the US, obesity prevalence is more of a lifestyle issue that a socio-economic one. According to Bentley et al. (2018), both the rich and the poor experience diabetes as both a social issue and an economic issue. Clarke et al., (2021) looked at the dynamics of obesity based on the political science theory. The author argues that policy makers should be on the frontline in underscoring the existing correlation between obesity and economic abilities. An investment in such research initiatives help to underscore the government’s need to initiative fairer economic policies to reduce obesity.

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Data to be Used and the Variable Analysis

The data to be used for this research will be from healthcare organizations. Based on the patient records in the facilities, it is possible to underscore the most contributing factors for diabetes. For instance, the patient record book can indicate the patient’s employment status or general socioeconomic status. The records can show within a given span of time, how many patients have been diagnosed with diabetes and what is the economic status of the patients.

The variables that will be used for this research will be household income against diabetes rates in the identified health organizations within a given span of time. Comparing the household income levels and the prevalence rates of diabetes among patients in a given health organization helps to provide a clear response to the research question. The proposed time frame will be a five-year window period. Looking at the family income provides an extensive understanding of how the high- and low-income earners are affected when it comes to getting diabetes and other related chronic conditions.

Regression Equation and Causal Variable

Regression equations are important in assessing the rate of diabetes as well as in predicting the likely prevalence after a given time period. The regression equation model which can be used to predict the diabetes rates among victims of diabetes is as shown below

Diabetes rate (Dt) = ∆Xt + C;

In the equation Xt defines the natural logarithm of the median household income in a given year period t while c is the y-intercept. The equation is adopted as used by Bentley et al., (2018).

The dependent variable obesity was measured using the body mass index (BMI) which is conventionally accepted by WHO as a measure of one’s body weight. The independent variable is the family income which eventually defines the quality of life that a family is likely to live and which defines the prevalence of obesity levels.

Economic Model To Be Used

The economic model that will be used to explore the correlation between family income and the prevalence of obesity is the classical economic theory model. According to this model, there are classical factors that need to be considered when assessing the prevalence of obesity. The model states that some of the key issues that need to be assessed include unemployment, income below the identified poverty line and the general income of patients. The classical model delves into the socioeconomic status to evaluate the key factors that explain the increasing cases of obesity among a specific target population.

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